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SUMMARY:A machine learning approach to duality in statistical physics - An
 drea Ferrari (Deutsches Elektronen-Synchrotron)
DTSTART:20251120T100000Z
DTEND:20251120T110000Z
UID:TALK240649@talks.cam.ac.uk
DESCRIPTION:The notion of duality --the fact that a physical system enjoys
  surprisingly different&nbsp\; descriptions-- is a key driver of modern th
 eoretical physics. In this talk I will formulate the task of duality disco
 very in statistical physics as an optimisation problem that generalises th
 e more standard one of fitting parameters in a Hamiltonian. I will show ho
 w a simple version of this problem can be solved to obtain an automated re
 discovery of the celebrated Kramers-Wannier duality for the 2d Ising model
 . If time will permit\, I will conclude with some preliminary results conc
 erning more complicated models\, and discuss how the framework could be ap
 plied to investigate unknown or poorly known dualities.
LOCATION:Seminar Room 1\, Newton Institute
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